This paper proposes a thresholding based segmentation method aided by Kleene Algebra. For a given image including some regions of interest (ROIs for short) with the coherent intensity level, assume that we can segment each ROI on applying thresholding technique. Three segmented states are then derived for every ROI: Shortage denoted by logic value 0, Correct denoted by 1 and Excess denoted by 2. The segmented states for every ROI in the image can be then expressed on a ternary logic system. Our goal is then set to find "Correct (1)" state for every ROI. First, unate function, which is a model of Kleene Algebra, based procedure is proposed. However, this method is not complete for some cases, that is, correctly segmented ratio is about 70% for three and four ROI segmentation. For the failed cases, Brzozowski operations, which are defined on De Morgan algebra, can accommodate to completely find all "Correct" states. Finally, we apply these procedures to segmentation problems of a human brain MR image and a foot CT image. As the result, we can find all "1" states for the ROIs, i. e. , we can correctly segment the ROIs.

In this paper, we discuss problems in design and fault masking of multiple-valued cellular arrays where basic cells having simple switch functions are arranged iteratively. The stuck-at faults of switch cells are assumed to be fault models. First, we introduce a universal single-level array and derive the ratio of the number of single faults whose influence can be masked to the total number of single faults. Next, we propose a universal two-level array that outputs correct values even if single faults occur in it and derive the ratio of the number of double faults whose influence can be masked compared to the total number of double faults. By evaluating the universal single-level array and the universal two-level array from the viewpoints of design and fault masking, we show that the latter is superior to the former. Finally, we compare our universal two-level array with formerly presented arrays in order to demonstrate the advantages of our universal two-level array.

This paper proposes a repairable and diagnosable k-valued cellular array. We assume a single fault, i.e., either stuck-at-O fault or stuck-at-(k1) fault of switches occurs in the array. By building in a duplicate column iteratively, when a stuck-at-(k1) fault occurs in the array, the fault never influences the output of the array. That is, we can construct a fault-tolerant array for the stuck-at-(k1) fault. While, for the stuck-at-O fault, the diagnosing method is simple and easy because we don't have to diagnose the stuck-at-(k1) fault. Moreover, our array can be repaired easily for the fault. The comparison with other rectangular arrays shows that our array has advantages for the number of cells and the cost of the fault diagnosis.

This paper describes the benefit of utilizing the unary function generators in a multiple-valued Programmable Logic Array (PLA). We will clarify the most suitable PLA structure in terms of the array size. The multiple-valued PLA considered here has a structure with two types of function generators (literal and unary function generators), a first-level array and a second-level array. On investigating the effectiveness to reduce the array size, we can pick up four form PLAs: MAX-of-TPRODUCT form, MIN-of-TSUM form, TSUM-of-TPRODUCT form and TPRODUCT-of-TSUM form PLAs among possible eight form PLAs constructing from the MAX, MIN, TSUM and TPRODUCT operators. The upper bound of the array sizes with v UGs is derived as (log2ppv + p(n-v) + 1) pn-1 to realize any n-variable p-valued function. Next, experiments to derive the smallest array sizes are done for 10000 randomly generated functions and 21 arithmetic functions. These results conclude that MAX-of-TPRODUCT form PLA is the most useful in reducing the array size among the four form PLAs.

To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.

To enhance fault tolerance ability of the feedforward neural networks (NNs for short) implemented in hardware, we discuss the learning algorithm that converges without adding extra neurons and a large amount of extra learning time and cycles. Our algorithm modified from the standard backpropagation algorithm (SBPA for short) limits synaptic weights of neurons in range during learning phase. The upper and lower bounds of the weights are calculated according to the average and standard deviation of them. Then our algorithm reupdates any weight beyond the calculated range to the upper or lower bound. Since the above enables us to decrease the standard deviation of the weights, it is useful in enhancing fault tolerance. We apply NNs trained with other algorithms and our one to a character recognition problem. It is shown that our one is superior to other ones in reliability, extra learning time and/or extra learning cycles. Besides we clarify that our algorithm never degrades the generalization ability of NNs although it coerces the weights within the calculated range.

In this paper we propose a learning algorithm to enhance the fault tolerance of feedforward neural networks (NNs for short) by manipulating the gradient of sigmoid activation function of the neuron. We assume stuck-at-0 and stuck-at-1 faults of the connection link. For the output layer, we employ the function with the relatively gentle gradient to enhance its fault tolerance. For enhancing the fault tolerance of hidden layer, we steepen the gradient of function after convergence. The experimental results for a character recognition problem show that our NN is superior in fault tolerance, learning cycles and learning time to other NNs trained with the algorithms employing fault injection, forcible weight limit and the calculation of relevance of each weight to the output error. Besides the gradient manipulation incorporated in our algorithm never spoils the generalization ability.

This paper presents a feature extraction model MAGNET' to find the deepest point of branched sulcus. Our model demonstrates magnetic principle and consists of four types of ideal magnetic poles: an N-pole and three S-poles. According to attractive or repulsive Coulomb forces between their poles, one of the S-poles is pushed to the deepest point of the sulcus. First, we explain our model on the simple sulcus model. Second, we apply it to the sulcus with implicit branches. Our model can detect the target points in every branch. Then an example to realize the model on a synthetic image is introduced. We apply our model to human brain MR images and human foot CT images. Experimental results on human brain MR images show that our method enable us to successfully detect the points.

In this paper, we propose a method to design ternary cellular arrays by using Ternary Decision Diagrams (TDD's). Our cellular array has a rectangular structure composed of ternary switch cells. The ternary functions represented by TDD's are realized by mapping the TDD's to the arrays directly. That is, both the nodes and the edges in the TDD are realized by some sets of the cells. Since TDD's can represent easily multiple-output functions without large memory requirements, our arrays are wuitable for the realization of multiple-output functions. To evaluate our method, we apply our method to some benchmark circuits, and compare our arrays with the ternary PLA's. The experimental results show that our arrays have the advantage for their sizes, especially in the realization of symmetric functions. The results also clarify that the size of our arrays depends on the size of TDD's.

A block-matching-based self-organizing map (BMSOM) is presented. Finding a winner is carried out for each block, which is a set of neurons arranged in square. The proposed learning process updates the reference vectors of all of the neurons in a winner block. Then, the degrees of vector modifications are mainly controlled by the size (i.e., the number of neurons) of the winner block. To prevent a single cluster with neurons from splitting into some disjointed clusters, the restriction of the block size is imposed in the beginning of learning. At the main stage, this restriction is canceled. In BMSOM learning, the size of a winner block does not always decrease monotonically. The formula used to update the reference vectors is basically uncontrolled by time. Therefore, even if a map is in a nonstationary environment, training the map is probably pursued without interruption to adjust time-controlled parameters such as learning rate. Numerical results demonstrate that the BMSOM makes it possible to improve the plasticity of maps in a nonstationary environment and incremental learning.

A method is proposed for realizing any k-valued n-variable function with a celluler array, which consists of linear arrays (called input arrays) and a rectangular array (called control array). In this method, a k-valued n-variable function is divided into kn-1 one-variable functions and remaining (n1)-variable function. The parts of one-variable functions are realized by the input arrays, remaintng the (n1)-variable function is realized by the control array. The array realizing the function is composed by connecting the input arrays with the control array. Then, this array requires (kn2)kn-1 cells and the number is smaller than the other rectangular arrays. Next, a ternary cell circuit and a literal circuit are actually constructed with CMOS transistors and NMOS pass transistors. The experiment shows that these circuits perform the expected operations.

In this paper we propose a MIN (Multistage Interconnection Network) whose performance in the faulty case degrades as gracefully as possible. We focus on a two-dilated baseline network as a sort of MIN. The link connection pattern in our MIN is determined so that all the available paths established between an input terminal and an output terminal via an identical input of a SE (Switching Element) in some stage will never pass through an identical SE in the next stage. Extra links are useful in improving the performance of the MIN and do not complicate the routing scheme. There is no difference between our MIN and others constructed from a baseline network with regard to numbers of links and cross points in all SEs. The theoretical computation and simulation-based study show that our MIN is superior to others in performance, especially in robustness against concentrated SE faults in an identical stage.

Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (γ-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, γ-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.